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There is no single RAM or VRAM requirement for local AI. The right amount depends on the exact model, its quantization and file format, context length, runtime overhead, and whether you use a GPU, CPU, or unified memory. Start with the model’s documented memory guidance, then leave headroom for the operating system, other applications, context cache, and the inference engine. OpenAI’s published examples show the spread: gpt-oss-20b is described as running on edge devices with 16 GB of memory, while gpt-oss-120b is described as running efficiently on one 80 GB GPU. Those figures apply to those named models and workflows, not to every model with a similar parameter count.
What is the short answer for local AI memory?
Choose memory for a specific model and workload, not for “AI” in general. Obtain the actual quantized model file, check the runtime’s requirements, and add capacity for the context window, temporary buffers, and everything else running on the machine.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteA model’s advertised parameter count is only one input. Two models with similar counts can have different weight formats, architectures, tokenizer data, cache behavior, and software requirements. A setup that loads a model may still fail when you increase the context length, open a second session, or leave too little memory for the operating system.
Why isn’t the model-file size the whole memory budget?
The weights are the largest item for many inference jobs, but they are not the only allocation. The runtime must reserve working buffers, and the model’s attention cache grows as it processes more tokens.
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Context length creates KV-cache demand
During generation, the key-value (KV) cache stores attention information for the prompt and generated tokens. A 128k-token context can require substantially more memory than a short chat, even when the weights never change. The exact cache size depends on the model architecture, numeric format, number of layers, and runtime implementation, so there is no reliable context multiplier that applies to every model.
Runtime and application overhead remain resident
Inference engines allocate scratch space, graph or kernel work areas, tokenization data, and other buffers. The operating system, desktop, browser, monitoring tools, and background services also consume memory. If a model nearly fills a GPU or unified-memory system at load time, there may be too little headroom for a useful context or stable operation.
Concurrent sessions multiply the pressure
Running two conversations, a server with several users, or a batch of prompts can require separate cache or scheduling allocations. Size for the number of simultaneous sessions you actually intend to support rather than for a single demonstration prompt.
Are system RAM, VRAM and unified memory interchangeable?
No. They are different pools with different performance and capacity constraints, although some runtimes can move data between them.
| Memory pool | What it is used for | Important limitation |
|---|---|---|
| GPU VRAM | Model weights, KV cache, and GPU work buffers when layers run on a discrete graphics processor. | Capacity is fixed on the card. A model can fail to load when only a small amount remains free, even if system RAM is plentiful. |
| System RAM | CPU inference, model storage while loading, operating-system activity, and CPU-offloaded layers. | CPU execution and transfers can be much slower than keeping the working set in VRAM; available RAM is lower than the installed total. |
| Unified memory | A shared pool used by the processor, integrated graphics, operating system, and applications. | Every application competes for the same pool, so the advertised capacity is not all available to the model. |
GPU offload can split a model between VRAM and RAM, but that is a deployment choice, not a way to turn the two pools into identical high-speed memory. The inference engine and hardware driver must support the selected model format and offload path.
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How does quantization change RAM and VRAM requirements?
Quantization stores weights with fewer bits, reducing their footprint and often making local inference possible on smaller devices. It can also change output behavior and kernel support, so a smaller file is not automatically the best choice.
Ollama’s current gpt-oss model documentation describes MXFP4 quantization at 4.25 bits per parameter for most of the model’s mixture-of-experts weights; those weights account for more than 90% of the total parameter count. That is a model-specific example, not a universal conversion factor. Other models may use 8-bit, 6-bit, 4-bit, or different formats, and metadata plus non-quantized tensors add to the file.
Use the exact file size and runtime notes for the quantization you plan to run. Treat any hand calculation from parameter count and bits per parameter as a rough weight estimate only: it excludes cache, buffers, non-quantized components, allocator behavior, and the operating system.
What do published model and GPU examples actually show?
The following figures are named recommendations or publisher claims, not universal minimums. They illustrate how widely requirements vary.
| Source and workload | Published memory figure | What the figure means |
|---|---|---|
| OpenAI gpt-oss-20b on edge devices (2025) | 16 GB of memory | OpenAI says this model can run on edge devices with 16 GB for on-device use and local inference. It has 21 billion total parameters and a 128k context length. |
| OpenAI gpt-oss-120b (2025) | One 80 GB GPU | OpenAI describes the model as running efficiently on a single 80 GB GPU. It has 117 billion total parameters and a 128k context length. |
| NVIDIA local-inference guide | 8–16 GB VRAM | NVIDIA’s entry tier for models and workflows listed in that guide; it is not a guarantee for every model in that size range. |
| NVIDIA local-inference guide | 16–24 GB VRAM | A middle capacity tier in NVIDIA’s recommendations for its listed workflows. |
| NVIDIA local-inference guide | 24–48 GB VRAM | A higher-capacity tier in the same guide, useful when larger weights, longer contexts, or more headroom are required. |
OpenAI’s 16 GB and 80 GB examples should be read as guidance for two specific model families. NVIDIA’s tiers organize its own workflow recommendations; they do not establish that a particular parameter count always fits a particular VRAM number.
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How do you calculate the memory you actually need?
Use a workload-first checklist. It is more dependable than applying one “parameters times multiplier” rule to every model.
- Name the exact model and release. Record the model family, total parameters, architecture, context limit, and the runtime you intend to use. Mixture-of-experts models can list a large total parameter count while activating only part of the network for each token, so total parameters alone do not predict the allocation.
- Select the exact quantization and format. Note whether the file is MXFP4, 8-bit, 6-bit, 4-bit, or another format, and whether the runtime supports it natively. Use the published file size as the starting weight requirement.
- Set a realistic context length. A 128k maximum does not mean every session needs 128k. Choose the prompt and response length your application will actually permit, then consult the runtime’s cache guidance.
- Decide where computation will run. Full GPU placement needs enough free VRAM for weights, cache, and buffers. CPU inference needs enough system RAM and accepts a different speed profile. Partial offload needs both pools and a supported implementation.
- Add headroom. Reserve memory for the operating system, desktop, browser, monitoring, and other services. Leave additional room if you will run multiple sessions, tools, retrieval, image inputs, or a larger context later.
- Validate on the target platform. Confirm driver, operating-system, runtime, and model-format support. Check free memory after the system has booted, not just the capacity printed on a specification sheet.
Is a 24 GB graphics card enough for local AI?
Twenty-four gigabytes of VRAM is a useful capacity tier, but it is not a compatibility promise. NVIDIA places 24 GB within a 24–48 GB recommendation band for workflows in its local-inference guide. Whether a particular model fits depends on its quantized weights, context, cache, buffers, and the amount of VRAM already occupied by the display or other applications.
Before buying a 24 GB card, check the model’s downloadable file size and runtime documentation. A file that consumes nearly all 24 GB may load only with a short context, reduced concurrency, or CPU offload. A smaller quantized file can leave room for a longer context and more stable operation, but quantization quality and kernel support still matter.
For a purchase decision, compare the available VRAM after the operating system and graphics workload, not the nominal number alone. Also verify that the card’s software stack supports the model format; capacity without compatible kernels does not produce a usable deployment.
When should you use CPU inference or GPU offload?
CPU inference is appropriate when the model fits in system RAM and maximum throughput is not the priority. It avoids a hard VRAM ceiling but can be slower, especially for long contexts or many concurrent users.
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GPU offload is a compromise: some layers or tensors remain in RAM while others run in VRAM. It can make a model usable when no single memory pool is large enough, but transfers across the bus add latency and the exact behavior depends on the engine. Full GPU placement generally simplifies capacity planning for a supported model, while offload trades speed and complexity for flexibility.
Unified-memory machines require a separate calculation. The model, operating system, graphics subsystem, and applications all draw from the same pool, so leave a larger practical margin than the headline capacity suggests.
What happens when a model does not fit?
A failed allocation, out-of-memory message, or crash usually means the selected combination of weights, context, cache, and buffers exceeds the free pool. Reduce the largest controllable allocations first.
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- Close GPU-using applications and confirm free VRAM with the platform’s monitoring tools.
- Choose a smaller or more aggressively quantized model, while checking quality and format support.
- Lower the maximum context and limit simultaneous sessions.
- Move selected layers to system RAM if the runtime supports CPU offload, understanding that speed may fall.
- Restart the runtime after a failed load so cached allocations are released.
- Update the inference engine and driver only after verifying that the new versions support the model format; a version change can alter memory behavior.
If the model loads but generation becomes unstable at longer prompts, the weights probably fit while the KV cache or runtime buffers do not. Set a lower context limit and test again before replacing hardware.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare two local-AI hardware setups?
Compare the complete deployment, not just the largest memory number.
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| Comparison question | Why it matters |
|---|---|
| How much memory is free after boot? | Installed RAM or VRAM overstates what the model can use. |
| Which model file and quantization? | Formats change weight size, quality, and kernel compatibility. |
| What context and concurrency are required? | KV-cache and session allocations grow with active tokens and users. |
| Full GPU, CPU, or partial offload? | Placement changes both the pools required and expected responsiveness; no universal speed multiplier applies. |
| Which platform and runtime? | Drivers, operating systems, and inference engines support different formats and acceleration paths. |
A system with less VRAM but a well-supported quantized model may be more practical than a larger card that cannot run the required format. Conversely, abundant system RAM does not compensate for a VRAM-limited full-GPU plan unless offload is supported and acceptable.
Can one memory rule cover every local language model?
No. Exact minimum memory remains model-, runtime-, context-, and platform-specific. There is no generally applicable published statistic that establishes one RAM or VRAM minimum for all local models.
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Use the model publisher’s documentation and the inference engine’s installation notes as the authority for a specific build. Recheck them when model tags, quantization options, drivers, or software recommendations change. Hardware availability and supported formats also vary by region and date, so a current specification page matters more than an old forum estimate.
Frequently Asked Questions
Can a computer with 16 GB of RAM run a local AI model?
It can run some models, but the answer depends on the model file, quantization, context, and how much memory the operating system leaves free. OpenAI’s 16 GB statement applies specifically to gpt-oss-20b on edge devices, not to every model or runtime.
Does a larger context window make generation faster?
No. A larger context generally increases KV-cache use and the amount of prompt work. It may be necessary for your application, but it should be treated as a capacity requirement rather than a speed upgrade.
Can I combine two graphics cards to get their VRAM capacities?
Only when the inference software and model support multi-GPU placement. Memory is not automatically pooled across cards, and communication overhead can change performance, so verify the engine’s documented multi-GPU path before planning around the sum.
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Check system RAM and GPU VRAM after your normal desktop applications are open, then compare the free figures with the model file and runtime guidance. Repeat the check while using your intended context length because cache allocations occur during inference.
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